Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	North Dakota	139,642
2	Rhode Island	137,845
3	South Dakota	137,511
4	Utah	123,020
5	Tennessee	120,845
6	Arizona	117,665
7	Iowa	114,702
8	Oklahoma	112,774
9	Nebraska	112,646
10	Wisconsin	112,441
11	New Jersey	111,524
12	South Carolina	111,143
13	Arkansas	110,750
14	Alabama	107,083
15	Indiana	106,218
16	Kansas	106,088
17	Delaware	105,022
18	Mississippi	104,207
19	Idaho	104,126
20	Illinois	103,891
21	New York	103,643
22	Florida	102,014
23	Nevada	101,383
24	Montana	100,894
25	Georgia	100,336
26	Minnesota	99,740
27	Wyoming	99,545
28	Kentucky	99,138
29	Texas	98,917
30	Massachusetts	98,423
31	Louisiana	97,874
32	Missouri	96,342
33	California	94,343
34	Connecticut	93,605
35	New Mexico	93,469
36	North Carolina	91,672
37	Alaska	90,725
38	Ohio	90,692
39	Michigan	90,160
40	Pennsylvania	88,122
41	Colorado	86,740
42	West Virginia	83,859
43	Virginia	76,267
44	Maryland	72,808
45	New Hampshire	68,331
46	District of Columbia	66,652
47	Washington	51,676
48	Puerto Rico	49,731
49	Maine	43,793
50	Oregon	42,228
51	Vermont	35,777
52	Hawaii	22,584

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	Michigan	600
2	New Jersey	358
3	Pennsylvania	355
4	Rhode Island	334
5	Maine	328
6	Colorado	322
7	Minnesota	281
8	Florida	277
9	New Hampshire	276
10	North Dakota	253
11	Delaware	246
12	Alaska	243
13	New York	243
14	Illinois	229
15	Puerto Rico	225
16	Nebraska	222
17	Connecticut	217
18	Massachusetts	213
19	West Virginia	212
20	Washington	204
21	Oregon	192
22	Maryland	183
23	Tennessee	182
24	North Carolina	179
25	South Dakota	174
26	Alabama	172
27	Iowa	164
28	Montana	161
29	Ohio	151
30	Virginia	151
31	Indiana	149
32	Nevada	145
33	Wisconsin	144
34	South Carolina	143
35	Texas	143
36	Utah	143
37	Kentucky	142
38	District of Columbia	141
39	Wyoming	135
40	Georgia	133
41	Idaho	133
42	Louisiana	118
43	Missouri	118
44	Vermont	113
45	Mississippi	102
46	Arizona	91
47	New Mexico	81
48	Arkansas	78
49	Kansas	63
50	California	58
51	Oklahoma	50
52	Hawaii	46

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	2,846
2	New York	2,636
3	Massachusetts	2,541
4	Rhode Island	2,510
5	Mississippi	2,410
6	Arizona	2,365
7	Connecticut	2,254
8	Louisiana	2,220
9	South Dakota	2,211
10	Alabama	2,207
11	Pennsylvania	2,025
12	North Dakota	1,988
13	Indiana	1,968
14	New Mexico	1,919
15	Illinois	1,898
16	Arkansas	1,892
17	Iowa	1,871
18	Michigan	1,826
19	South Carolina	1,823
20	Georgia	1,813
21	Tennessee	1,762
22	Nevada	1,753
23	Texas	1,723
24	Kansas	1,716
25	Oklahoma	1,697
26	Delaware	1,647
27	Ohio	1,628
28	Florida	1,615
29	West Virginia	1,566
30	District of Columbia	1,555
31	California	1,550
32	Missouri	1,493
33	Montana	1,456
34	Kentucky	1,453
35	Maryland	1,426
36	Wisconsin	1,277
37	Minnesota	1,264
38	Virginia	1,248
39	Wyoming	1,218
40	Nebraska	1,215
41	North Carolina	1,195
42	Idaho	1,135
43	Colorado	1,104
44	New Hampshire	936
45	Washington	718
46	Puerto Rico	700
47	Utah	679
48	Oregon	588
49	Maine	572
50	Alaska	434
51	Vermont	389
52	Hawaii	334

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	Michigan	8
2	New Jersey	4
3	Pennsylvania	4
4	West Virginia	4
5	Arizona	3
6	Florida	3
7	Kentucky	3
8	Montana	3
9	Nevada	3
10	New York	3
11	Puerto Rico	3
12	Alabama	2
13	California	2
14	Connecticut	2
15	Georgia	2
16	Illinois	2
17	Louisiana	2
18	Maryland	2
19	Mississippi	2
20	Missouri	2
21	New Mexico	2
22	North Carolina	2
23	North Dakota	2
24	Rhode Island	2
25	South Carolina	2
26	Tennessee	2
27	Texas	2
28	Virginia	2
29	Arkansas	1
30	Colorado	1
31	Indiana	1
32	Iowa	1
33	Massachusetts	1
34	Minnesota	1
35	Nebraska	1
36	Ohio	1
37	Oklahoma	1
38	South Dakota	1
39	Utah	1
40	Washington	1
41	Wyoming	1
42	Alaska	0
43	Delaware	0
44	District of Columbia	0
45	Hawaii	0
46	Idaho	0
47	Kansas	0
48	Maine	0
49	New Hampshire	0
50	Oregon	0
51	Vermont	0
52	Wisconsin	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Crowley	Colorado	356,212	1	99
Chattahoochee	Georgia	349,684	2	99
Bent	Colorado	266,810	3	99
Dewey	South Dakota	245,757	4	99
Lincoln	Arkansas	245,623	5	99
Davidson	Tennessee	140,491	185	94
Richland	South Carolina	109,268	1007	67
York	South Carolina	106,894	1099	65
Orange	California	84,817	2137	31
Pierce	Washington	52,108	2891	7

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Foard	Texas	8,658	1	99
Galax city	Virginia	8,035	2	99
Jerauld	South Dakota	7,948	3	99
Emporia city	Virginia	7,856	4	99
Hancock	Georgia	7,686	5	99
Orange	California	1,546	1821	42
Davidson	Tennessee	1,335	2071	34
York	South Carolina	1,317	2094	33
Richland	South Carolina	1,299	2116	32
Pierce	Washington	703	2746	12

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.